Plenary Speaker slides at the 2016 International Workshop on Biodesign Automation

60IWBDA 2016 - Newcastle Upon Tyne /
Accelerating Synthetic Biology
via Software and Hardware
Advances
Prof. Natalio Krasnogor
Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group
Centre for Bacterial Cell Biology
Centre for Synthetic Biology and the Bioeconomy
Newcastle University
Natalio.Krasnogor@newcastle
http://homepages.cs.ncl.ac.uk/natalio.krasnogor/
twitter: @NKrasnogor
1
Tuesday, 6 September 16

Outline
• Computational & Hardware support for designing and
manufacturing Combinatorial DNA at your Desk
• Machine Intelligence for Synthetic Biology
•Conclusions
2

N
• Different scales require different “programming
languages”, e.g. DNALD, SBOL, IBL, etc for
modularity, hierarchical abstraction, reusability
& standardisation across scales
• Microfluidics for writing DNA but also as a
“wind-tunnel” on your desktop, e.g.,:
• to try out multiple designs and gather data
• to optimise cell-free kits for ad-hoc
applications
• to combinatorial stress-test synthetic cell
systems
• Machine Intelligence & data analytics across
scales
3

60IWBDA 2016 - Newcastle Upon Tyne /4

Reading and Writing DNA at your Desk
• The study of biology has accelerated rapidly thanks to
methods for massively parallel cell-free cloning and DNA
sequencing in desktop next generation sequencing (NGS)
machines
• The engineering of biology is still largely restrained by
limitations of gene synthesis and cloning methodologies
• Off-the-shelf Microfluidic is about to supercharge synthetic
biology by:
• increasing the throughput of gene synthesis
• reducing cost through miniaturization
• handle complexity of more ambitious designs through
autonomous liquid handling at source.
5

Combinatorial DNA Synthesis on your Desktop
Parts
Library
Targets
Operators
Planer Assembly Plan
Instrument Instructions
Programable Order
Polymerization (POP)
Microfluidics Combinatorial
Assembly of DNA (M-CAD)
Microfluidics In Vitro
Cloning (MIC)
Key challenge is to enable
precise design, editing and
manufacturing of combinatorial
DNA libraries at your desk.
CAD
CAM
6

Parts
Library
Targets
Operators
Programable Order
Cloning (MIC)
CAD
CAM
and then find
out what the
heck just
happened!?!?
6

A Programming Language for Sequences:
DNALD (DNA Library Design)
A specification language that
produces a set of target DNA
sequences as a function of
operations on a set of inputs
To maximise impact the specification process must be:
• user friendly and debuggable
• but expressively powerful enough to:
• define non-trivial combinatorial constructs
• communicate degrees of freedom
7

DNA Library Designer with
DNALD
8

Background validation
evaluation
constraints
syntax
errors
error
navigation
errors
marked
9

Suggests quick fixes
resolve names
correct indices
10

Search across projects
search
results
navigate
workspace
11

Compare differences
between files and versions
duplicate each or
every change
highlights insertions
and deletions
12

Graphical Representation of
Complex DNA Libraries
Assembly plan
13

And Paired Visualisations
l Emphasises reuse with shared nodes and provides
indication of library's combinatorial degree
l Every path from 5' to 3' is an output
Graphical Representation of
Complex DNA Libraries
14

How can DNALD be extended?
• Plug-ins could be added to add semantics to variants, eg:
• different codon usage or codon tables for same protein sequence
• different coded protein sequence with same physico-chemical
properties
•
Equivalent/Reduced Alphabets
for Contact Number preservation
Equivalent/Reduced Alphabets
for solvent accessibility preservation
Text
Automated Alphabet Reduction for Protein Datasets. BMC Bioinformatics, 2009, 10:6
15

• Plug-ins could be added to use eg:
• Statistical or machine learning driven design of experiments
Text
16

Planning heuristics adaptable to other assembly protocols
17

EXAMPLES
18

Stem Cell Reprogramming (UKB)
Frank Edenhofer
19

Operons Rewiring (UEVE)
François Képès
20

From the DNA Library to the Synthesis Plan
l When O={+} & P=unrestricted è
Planning problem
l Related computational problem
bounded-depth min-cost string
production (BDMSP) is NP-hard
and APX-hard by reduction from
vertex cover
21

Parts
Library
Targets
Operators
Programable Order
Cloning (MIC)
CAD
CAM
22

One Pot VS Generic Protocols
Microfluidic gene synthesis is advancing fast to:
• overcome the limitation of strictly assembling genes
in one pot reactions & accommodate a range of
assembly methods.
• be able to execute ad hoc gene synthesis via
programmability over droplet routing.
• enable the implementation of complex & parallel
schemes (which are challenging to execute both
manually and on liquid handling robots)
• able to accommodate different construction protocols.
•More reproducible
Zhou,X et al. Microﬂuidic
PicoArray synthesis of
oligodeoxynucleotides and
simultaneous assembling of
multiple DNA sequences.
Nucleic Acids Res., 32,
5409–5417. 2004
Tian,J., et al. .
Advancinghigh-throughput
gene synthesis technology.
Mol. Biosyst., 5, 714–722.
2009
Quan,J.,et al. Parallel on-chip
gene synthesis and
application to optimization of
protein expression. Nat.
Biotechnol., 29, 449–452.
2011
24

Programmable Liquid Handling
25

What Can Be Done
• Synthesis of Genes de novo ==> POP assembly
• Construction of Rationally Designed (DNALD) Combinatorial
Gene Libraries ==> M-CAD
• Cell-free cloning of assembled synthetic DNA ==> M-IC
• Sequenced validation
• Downstream (application) validation
on-chip
off-chip
27

Post-transcriptional regulation of Azurin, a bacterial QS-
activated gene (Nottingham & Newcastle)
Koch, Heeb, Camara, Dubern, Krasnogor
28

Combinatorial Library Design (DNALD) &
Construction (EWD): the Azurin example
• Bacteria regulate gene expression at the transcriptional
and post- transcriptional level
• RsmA global post-transcriptional regulator, modulates
switch between acute and chronic infection (p. aeruginosa
@ cystic fibrosis)
• RsmA positively and negatively regulates target mRNAs
by binding to mRNA secondary structures (stem loops-
palindromic sequences)
•RsmA homologues (CsrA) present in a variety of bacteria,
Gram-positive and Gram-negative
29

It is postulated that RmsA
positively regulates Azurin
Three hypothetical loops in the mRNA
2nd and 3rd AGGA is in the loop of the stem
30

It is postulated that RrmsA
31

32

33

Post-transcriptional regulation of Azurin, a bacterial QS-
activated gene (Nottingham & Newcastle)
Koch, Heeb, Camara, Dubern, Krasnogor
34

M-CAD
35

M-CAD
36

Results
•Gel electrophoresis analysis
of a representative set of 16 of
the Azurin library targets shows
that all constructs are of the
expected size with no spurious
assembly products
•Western blot from extracts of
Pseudomonas aeruginosa
expressing the azurine gene
incubated with anti-azurin
polyclonal antibodies
37

Outline
•Conclusions
38

Synthetic Polymers For Controlling QS
Dependent Phenotypes
Bacterial Sequestrant
Dual action
QS Quencher
39

10s of Persons-years!
40

On Roll Royces & Ford Ts: an analogy
• Hand-crafted
• + Comfortable
• + Reliable/Robust
• Faster
• + Expensive
• Selective
• Assembly Line Product
• - Comfortable
• - Reliable/Robust
• Slower
• Cheaper
• Ubiquitous/Popular
41

Modular models for SynBio design
http://www.virtualparts.org
42

What does the VPR do?
•Provides modular, composable,
dynamic models of genetic components
•AND includes models of the upper
layers of molecular biology they
encode (mRNA, proteins, metabolites
etc.)
•AND their interactions
•SBML and Rule Based
•Facilitates model-based design
•Supports automated design
•e.g. Computational Intelligence
•Supports CAD tools and languages
G. Misirli, J. Hallinan, and A. Wipat, “Composable modular models for synthetic
biology,” ACM J. Emerging Technologies in Computing Systems, vol. 11, iss. 3, pp. 1-19,
2014
43

Synthetic Biology Open Language (SBOL)
•Synthetic biology standard
(currently Version 2.0):
•Designed to allow for the
exchange of descriptions of
genetic parts, devices,
modules, and systems.
•Facilitates storage of genetic
designs in repositories.
•Allows for designs of genetic
parts and systems to be
embedded in publications.
•SBOL can be used to create
workflows between different
tools Galdzicki et al., Nature Biotechnology (2014)
Six independent groups collaborated on
the design of a set of genetic toggle
switches. using several SBOL enabled
tools.
44

An Environment for Augmented Biodesign Using Integrated Data Resources James McLaughlin, Goksel Misirli, Matthew Pocock, and Anil Wipat
IWBDA 2016

User
create data-informed design
Data augmented
design
AmBiT
Data enrichment:
BLAST
EMBOSS
database cross refs
Other SBOL Stack
instances
46
An Environment for Augmented Biodesign Using Integrated Data Resources James McLaughlin, Goksel Misirli, Matthew Pocock, and Anil Wipat
IWBDA 2016

Meta-Stochastic Simulation
ssapredict - Web application using
classifiers as a tool for biologists to
deduce the best stochastic simulation
algorithm for their model
User simply clicks to upload
stochastic model in SBML format
Fast model property analysis is
performed (C++ and igraph)
Algorithm prediction performed using
biomodels analysis. (Linear SVC
using python sklearn)
Results displayed. User can then
download preconfigured simulator to
execute their model
47

Automated Model Analysis for
Simulations Reaction & species
dependency graphs
generated from models
Clocks identify fast to
compute properties
48

Model analysis
49

•Model checking: Exhaustively, verifies whether a property holds by a model of
a system. Statistical model checking (SMC) integrates the simulation
technique with model checking by generating and verifying a number of
simulation paths to determine an “approximate correctness” of queried
properties.
•Machine Learning method for selecting the most appropriate Stochastic
Simulation Algorithms (SSAs) has been extended to Statistical Model Checkers
(SMCs) selection.
•However, there are intrinsic differences between simulation algorithms and
model checkers; model checkers require both the model & property
specifications.
•Our methodology is illustrated for frequently used properties in the literature,
called property patterns.
Automated Model Analysis for
Formal Verification
In collaboration with Prof. M. Gheorghe, Dr. Savas
Konur (Bradford University) & Mehmet E. Bakir
(Sheffield University)
50

Verification Patterns
•Patterns are frequently used property types for querying
features of models (e.g., something is always the case,
something will eventually be the case)
•Below are 8 frequently used patterns represented in natural
language and using existing temporal logic operators
51

Fastest model checkers
52

Fastest model checkers
53

SMCs Prediction
•The SSAs prediction method has been extended by allowing parallel edges
for species and reaction dependency graphs and some non-graph properties
such as, the number of updated variables involved in a reaction - min, mean,
max and sum of the update values.
•Support Vector Machine (SVM) prediction of the fastest SMC presented
below.
Patterns Accuracy
Eventually 0.945
Always 0.927
Follows 0.961
Precedes 0.967
Never 0.942
Steady-State 0.939
Until 0.941
Infinitely-Often 0.961
54

Outline
•Conclusions
55

u domain specific language for
synthetic biology
u SB entities (genes, proteins,
promoters) first class entities
u implemented as Eclipse RCP
Synthetic Biology Life Cycle
Design
u emphasis on high performance
u 9 different stochastic simulation
algorithm variants
u automated algorithm selection
u MPI support
Simulation
VerificationBiocompilation
u quasi-natural language for
definition of properties
u automatic translation into
temporal logics
u automated algorithm
selection
u links to sequence repositories
u design completion with terminators,
RBS, spacers, ...
u consideration of custom constraints
VERIFY [ GFP > 0 uM ] EVENTUALLY HOLDS
VERIFY [ GFP > 0 uM ] ALWAYS HOLDS
VERIFY [ GFP > 2*RFP ] NEVER HOLDS
GTATAATTACGGCTACAATGCGCCGTTATT
56

Design
Simulation
VerificationBiocompilation
Data Analytics &
Machine
Intelligence
“Wind Tunneling”
via desktop
microfluidics
57

58

SBOL files
DSL files
Computational logs
Engineering Protocols
Experimental logs
(seq, proteomics, metabolomics, optical, etc)
etc
58

SBOL files
DSL files
Computational logs
Experimental logs
etc
SBOL files
DSL files
Computational logs
Experimental logs
etc
58

SBOL files
DSL files
Computational logs
Experimental logs
etc
SBOL files
DSL files
Computational logs
Experimental logs
etc
SBOL files
DSL files
Computational logs
Experimental logs
(seq, proteomics, metabolomics, optic
etc
58

SBOL files
DSL files
Computational logs
Experimental logs
etc
SBOL files
DSL files
Computational logs
Experimental logs
etc
SBOL files
DSL files
Computationally logs
Experimental logs
etc
SBOL files
DSL files
Computational logs
Experimental logs
etc
58

SBOL files
DSL files
Computational logs
Experimental logs
etc
SBOL files
DSL files
Experimental logs
etc
SBOL files
DSL files
Computational logs
Experimental logs
etc
SBOL files
DSL files
Experimental logs
etc SBOL files
DSL files
Experimental logs
(seq, proteomics, metabolomic
etc
SBOL files
DSL files
Computational logs
Experimental logs
etc
58

SBOL files
DSL files
Computational logs
Experimental logs
etc
SBOL files
DSL files
Experimental logs
(seq, proteomics, metabolomics, optical,
etc
SBOL files
DSL files
Experimental logs
etc
SBOL files
DSL files
Computational logs
Experimental logs
etc
SBOL files
DSL files
Computational logs
Experimental logs
etc
SBOL files
DSL files
Experimental logs
etc
SBOL files
DSL files
Computational logs
Experimental logs
etc
SBOL files
DSL files
Experimental logs
etc SBOL files
DSL files
Experimental logs
(seq, proteomics, metabolomic
etc
SBOL files
DSL files
Computational logs
Experimental logs
etcSBOL files
DSL files
Experimental logs
etc
58

disrupted by machine learning, data analytic and peer-to-peer
data-driven bio-manufacturing
so we can
finally find out
what the heck
just
happened!?!?
Like “Neural
Grafting” for
BioRobots
59

My colleagues at the ICOS and CSBB in Newcastle
Prof. A. Wipat (Newcastle U.)
Dr. M. Gheorghe (Bradford U.)
Dr. J. Bacardit (Newcastle U.)
Prof. P. Wright (Newcastle U.)
Prof. C. Alexander (U. Nottingham)
Dr. F. Fernandez-Trillo (U. Birmingham)
Prof. M. Camara (U. Nottingham)
Dr. S. Heeb (U. Nottingham)
Dr. J. Dubern (U. Nottingham)
Prof. C. Biggs (U. Sheffield)
Dr. S. Konur (Bradford U.)
Dr. S. Kalvala (Warwick U.)
Dr. C. Ladrou (Warwick U.)
Dr. C. Delattre (Illumina)
Dr. A. Rivald (Illumina)
Prof. E. Shapiro (Weizmann Institute)
Dr. T. Ben Yehezquel (Weizmann Institute)
Prof. U. Feigel (Weizmann Institute)
!
Acknowledgements
EP/N031962/1
EP/J004111/2
EP/D021847/2
EP/I031642/2
BB/F01855X/1
BB/D019613/1
5 Years Research Managing Director
for a new £8M grant:
http://tinyurl.com/h99vl3h
closing date: 5/September/2016
60

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